xe
Execute a command once for each line piped from another command or file.
More information: home page
- Run a command once for each line of input data as arguments:
arguments_source | xe command
- Execute the commands, replacing any occurrence of the placeholder (marked as `{}`) with the input line:
arguments_source | xe command {} optional_extra_arguments
- Execute a shellscript, joining every `N` lines into a single call:
echo -e 'a\nb' | xe -N2 -s 'echo $2 $1'
- Delete all files with a `.backup` extension:
find . -name '*.backup' | xe rm -v
- Run up to `max-jobs` processes in parallel; the default is 1. If `max-jobs` is 0, xe will run as many processes as cpu cores:
arguments_source | xe -j max-jobs command


Camila Cabello says she loves Shawn Mendes after AMAs performance
Camila Cabello dropped the L-bomb on Shawn Mendes after their triumphant night at yesterday’s American Music Awards.
The Senorita singers performed an explosive rendition of their duet on the night, before winning the collaboration of the year gong.
After all the excitement, Camila took to Instagram to express how she really feels about her pop star boyfriend.
‘I love u @shawnmendes,’ she gushed.
She went there.
‘Thank u for being so there for me yesterday and every other day,’ she went on.
‘U have the most caring and beautiful heart in the world! thanks to all our fans for voting, we love and appreciate you guys so much and are so thankful for you.’
Too cute.
Camila, 22, also recently opened up about her relationship on Lorraine, saying: ‘I’m so happy.
Advertisement Advertisement
‘I’ve known him for such a long time and I don’t know, he just feels like home to me. Yeah, I’m really happy.’
She also confirmed that some of the songs on her upcoming second studio album Romance are about the Stitches singer – and that she never had time to fall in love before meeting Shawn.
Well, she did join Fifth Harmony on X Factor USA at the tender age of 16.
‘Like, from my first album to now, it’s probably what’s influenced my entire album,’ the Havana singer explained. ‘And it being called Romance and it being about falling in love, I literally did not have time to fall in love before the age of 20, because I was just working all the time.’
‘Everything from my first album was drawn from my imagination,’ she added. ‘I was single for like 20 years!’
Got a showbiz story?
If you've got a celebrity story, video or pictures get in touch with the Metro.co.uk entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


Everyone wants to have a good-looking website to represent them on
the internet, right? In this modern day and age, first impressions
are everything. That’s why having a presentable and user-friendly
website is very important to retain user attention as well as make
things as convenient as possible. On top of that, the world is
shifting towards a more mobile-diverse tech ecosystem with more
and more products being released into the smartphone market. That
being said, the rise of the mobile market calls for more websites
to be designed to look and feel good.
The topic I wish to do my research on is the mobile development
aspects of the Bootstrap framework which focuses on developing
mobile-first web applications. The term mobile-first is defined as
websites that can function and look good on a desktop-based
environment, but also function and look good on a mobile-based
environment. Bootstrap is a free and open-source front-end
framework that contains CSS and JavaScript-based design templates
for interface components that assist in making websites look great
and work on both desktop and mobile environments.
Bootstrap was developed by Mark Otto and Jacob Thornton. The duo
were employees at Twitter and were frustrated at the
inconsistencies and high maintenance burdens that the interface
development team had to deal with when it came to interface
development, so they developed a framework called Twitter
Blueprint to develop a front-end framework that unifies and brings
consistency to the team.
Installing Bootstrap is extremely simple to do. It is as simple as dragging and dropping an import into your
current project, or you can run a script in your favourite package manager to install it into your project.
Either method works and will give you full functionality to what Bootstrap has to offer. In this section,
I will show you how to install Bootstrap.
To manually install Bootstrap into your HTML project, simply copy this template into your HTML file and save! It's that simple.
To install using CLI, you can use the following commands for your preferred package manager:
Of all the frameworks out there, why Bootstrap? There are so many different libraries to choose from but why specifically Bootstrap? My reasoning is because Bootstrap is THE most popular CSS framework out there with over ~142 000 GitHub repositories utilizing Bootstrap, that's a lot! I chose Bootstrap because I do have some prior knowledge on it although very basic and I want to expand it and I saw that this Emerging Trends assignment would be a good opportunity to do so! I found that Bootstraps integration process, while very similar to other frameworks, is crazy easy and is as simple as a simple download and import. Then utilizing Bootstrap to its fullest potential is even easier. I think what makes Bootstrap so popular is because of how beautiful its components are as well as how simple and easy it is to implement into your HTML or CSS code.
Learning how to use Bootstrap was actually quite simple. A few
YouTube videos and a quick glance at the documentation was all I
needed to get started with Bootstrap! I used sites such as
this
and
this
as documentation guides to help me understand what Bootstrap has
to offer, then I watched videos such as
this
to help put it all together. I created a new GitHub repository and
then converted it into something called a GitHub Page, which is
essentially GitHub's free service of allowing users to host their
own website using the HTML code stored in the repository. From
there, I ran the usual downloads and imports to get Bootstrap
installed into my project and away I went! Implementing one thing
after another, testing things out as I went, it was a long and fun
learning process. The fun part of learning Bootstrap was reading
the official documentation and trying out the different components
that Bootstrap has to offer. Seeing everything show up as save and
open the HTML file has a certain rewarding feeling to it, as if
you're seeing the fruits of your labour.
Once I got comfortable with all the different components and
design templates, I began to start adding a bit of cohesion to my
project, I wanted to formulate an actual website and make it look
nice. The nice thing about Bootstrap is that every single
component in its library is optimized for mobile platforms, so
while my project will look nice on desktop, it will also function
and look perfectly fine on mobile as well! (Try opening this
website on your phone!)
Implementing everything was so easy because of Bootstraps column
and grid system, which is Bootstraps way of breaking down a
webpage into smaller squares that can be filled in with components
or text, which is utilized to make the desktop webpage look
beautiful on a mobile platform too. The grid system organizes
components in such a way that it utilizes as much space as the
website gives as possible to maximize screen space, and when the
screen space gets smaller for whatever reason such as scaling the
webpage down or viewing it on a mobile phone, the grid system
reorganizes itself to utilize the limited space as efficiently as
possible, which makes it possible for the desktop webpage to look
so good on a smaller screen.
With that knowledge in mind, I had no doubt that whatever I was
doing to the desktop webpage would look absolutely amazing on a
mobile platform.
The results of what I learned can be seen with this website you are reading this very blog post on! Everything on this website was created using Bootstrap and every component is a variation of a component given to me from the official documentation. Before Emerging Trends, I had this website created before as a personal project but it didn't have much on it. After being given the opportunity to go back and update this project, I was able to take this opportunity and optimize my website to make it mobile-friendly as well as add my blog posts to this site as a way to present my findings. I am quite proud of what I have accomplished so far and hope to further my research so that I can carry it over into the real world.


There are essentially two broad cases to consider in order to discuss WebSpecs. Either you have a bug against a specific aspect of a given specification, or you have a more general proposal that may be a new feature in an existing specification (WebSpec or not) or even a suggestion for a completely new WebSpec.
This is the easiest case. Bugs for specifications are handled in the GitHub issue tracker that goes with the repository. (Note that this applies to all parts of the projects, not just specs. For instance, this documentation can be improved in its own repository.) If you go to any WebSpec, right under the big title is a line with a list of links. Click on “bugs” and you’ll be right where you need to be.
The content of your bug report should be the same as for any other bug report. Be clear, concise, and complete. Point out what you expected and what you are seeing instead. Explain what the problem is based on a use case. Keep the HTML Design Principles in mind (even if your bug has nothing to do with HTML), notably the Priority of Constituencies. Be kind and polite (or else…).
There is only one small complicating factor compared to a typical bug report. Some WebSpecs will be forks of others, and in some situations the bug would be better filed on another one. If the distinction is obvious to you, just keep it in mind, otherwise just file on the document you were reading and people will move it if need be — better a bug in the wrong place than missed.
For obvious reasons, completely new ideas cannot be discussed as bugs against a given specification since presumably that specification does not exist in the first place.
Even in the case in which there happens to be an existing repository, if the novel idea you wish to discuss is broad enough in its reach it can be useful to bring it to the full community rather than restrict its discussion to the smaller number of people who will be tracking issues against a specific document.
In either case, the best place at which to conduct such a discussion is at the community's Discourse instance at http://discourse.specifiction.org/. (Note that it is expected to move to https://discourse.webplatform.org/ in the relatively close future.)
If you have used Web forums before and you still want to claw your own eyes out from the distress it has caused you, do not be alarmed. Discourse is forums reinvented. It is pleasant to use; in fact you will likely want to come back.
Simply create an account there and get started. Beginning users have a few restrictions on their accounts (such as number of links per post) in order to limit spam, but those get lifted quickly as you interact with others (and you can ask the admins to bump your karma).
Do not be afraid. No one will be mean to you if your post contains an error, or if you are suggesting something that has been explored and abandoned before. There are many Web standards and they have a rich history; no one is expected to know everything, and we would rather have good ideas at the cost of a little noise than the other way around.
There are only a few rules for participation. First, exchanges there are under our strict anti-jerk policy. If you are mean to others there will be consequences, if you see others being mean please report that to the admins (posts can be flagged). Second, the forum is for discussion of new ideas or problems with Web standards, not to ask questions about how to use something or how to make a browser work with your code. There's StackOverflow for that!
That's it! Several of the discussions there have already influenced existing and upcoming standards — all it takes for more of the same to happen is for you to join and talk.


CafesApi
@interface CafesApi : NodeDependant
Provides access to Textile cafes related APIs
-
Used to register a remote Textile Cafe node with the local Textile node
Declaration
Objective-C
- (void)register:(nonnull NSString *)url token:(nonnull NSString *)token completion:(nonnull void (^)(NSError *_Nonnull))completion;
Swift
func register(_ url: String, token: String, completion: @escaping (Error) -> Void)
Parameters
url
The url of the cafe being registered
token
The API token for the cafe being registered
completion
A block that will get called with an error
-
Used to deregister a previously registered Textile Cafe
Declaration
Objective-C
- (void)deregister:(nonnull NSString *)sessionId completion:(nonnull void (^)(NSError *_Nonnull))completion;
Swift
func deregister(_ sessionId: String, completion: @escaping (Error) -> Void)
Parameters
sessionId
The peer id of the cafe you want to deregister
completion
A block that will get called with an error
-
Used to refresh an individual Textile Cafe session
Declaration
Objective-C
- (void)refreshSession:(nonnull NSString *)sessionId completion: (nonnull void (^)(int *_Nullable, NSError *_Nonnull))completion;
Swift
func refreshSession(_ sessionId: String, completion: @escaping (UnsafeMutablePointer<Int32>?, Error) -> Void)
Parameters
sessionId
The peer id of the cafe who’s session you want to refresh
completion
A block that will get called with the results of the refresh operation
-
Check messages from all registered cafes
Declaration
Objective-C
- (void)checkMessages:(nonnull void (^)(NSError *_Nonnull))completion;
Swift
func checkMessages(_ completion: @escaping (Error) -> Void)
Parameters
completion
A block that will get called with an error
-
Fetches the CafeSession object for a previously registered Textile Cafe node
Declaration
Objective-C
- (id)session:(nonnull NSString *)peerId error:(NSError *_Nullable *_Nullable)error;
Swift
func session(_ peerId: String) throws -> Any
Parameters
peerId
The peer id of the previously registered cafe node
error
A reference to an error pointer that will be set in the case of an error
Return Value
The CafeSession for the previously registered cafe
-
Used to get sessions for all previously registered Textile Cafes
Declaration
Objective-C
- (id)sessions:(NSError *_Nullable *_Nullable)error;
Swift
func sessions() throws -> Any
Parameters
error
A reference to an error pointer that will be set in the case of an error
Return Value
An object containing a list of cafe sessions


vignettes/not-lost-in-translation-importing-and-exporting-graphs.Rmd
not-lost-in-translation-importing-and-exporting-graphs.Rmd
diffnet
objects (the core of netdiffuseR).diffnet
objects is not the only way to use netdiffuseR. Most of the functions can also be used with matrices and arrays.##
## Attaching package: 'netdiffuseR'
## The following object is masked from 'package:base':
##
## %*%
The issue is how to read it into R and handle it altogether.
Before start, we recommend the user to take a look at the Data input functions included in the utils
package (see ?read.table
), to the functions included in the foreign
package (useful to read from Stata, SPSS, etc.), and to the read_excel
function in the readxl
package1 for reading excel files into R.
edgelist_to_adjmat
edgelist_to_adjmat
supports both weights and spells.as_diffnet
For this example we will use the fakesurvey
and fakeEdgelist
datasets. The later was been generated using the fakesurvey
dataset, which holds survey information retrieved from 10 different individuals in two different groups. Ties in the fakeEdgelist
dataset are valued, and its value coincides with the number of nominatios that each individual in the survey did to each other.
Taking a look at fakesurvey
’s group
and id
column and fakeEdgelist
’s ego
and alter
columns the user can tell that the laters have been generated by adding up group*100
with id
.
# id group
# 1 1 1
# 2 2 1
# 3 3 1
# 4 4 1
# 5 5 1
# 6 1 2
# ego alter value
# 1 102 101 1
# 2 103 102 1
# 3 102 103 1
# 4 105 103 1
# 5 105 104 2
# 6 104 105 1
We will use this information later on to verify the way the data is sorted in the resulting diffnet
objects.
To use the as_diffnet
function we need at least two objects: a dynamic graph represented as either an array or a list of adjacency matrices, each of size \(n\times n\), which in our case will be \(10\times 10\), and an integer vector of size \(n=10\) which holds each vertex’s time of adoption. Lets start by generating the dynamic graph using th edgelist_to_adjmat
function:
# Coercing the edgelist to an adjacency matrix
adjmat <- edgelist_to_adjmat(
edgelist = fakeEdgelist[,1:2], # Should be a two column matrix/data.frame
w = fakeEdgelist$value, # An optional vector with weights
undirected = FALSE, # In this case, the edgelist is directed
t = 5) # We use this option to make 5 replicas of it
# Warning in edgelist_to_adjmat.matrix(as.matrix(edgelist), w, t0, t1, t, : Some edges a had NA/NULL value on either -times- or -w-:
# 11
# These won't be included in the adjacency matrix. The complete list will be stored as an attribute of the resulting adjacency matrix, namely, -incomplete-.
As the function warns, there is an edge that had incomplete information, and further was not used to create the adjacency matrix, the edge 11. If we take a look at that edge, we will see that indeed it had incomplete information on the weight attribute:
# ego alter value
# 11 202 <NA> NA
In order to address this, if we want to keep the vertex 202, an isolated vertex in the data, we need to fill that value up so that when creating the diffnet object we won’t have any problem having more attributes or times of adoption that vertices in the graph.
# Filling the empty data, and checking the outcome
fakeEdgelist[11,"value"] <- 1
fakeEdgelist[11,,drop=FALSE]
# ego alter value
# 11 202 <NA> 1
# Coercing the edgelist to an adjacency matrix (again)
adjmat <- edgelist_to_adjmat(
edgelist = fakeEdgelist[,1:2], # Should be a two column matrix/data.frame
w = fakeEdgelist$value, # An optional vector with weights
undirected = FALSE, # In this case, the edgelist is directed
keep.isolates = TRUE, # NOTICE THIS NEW ARGUMENT!
t = 5) # We use this option to make 5 replicas of it
As expected, there is no warning. Furthermore, we have told the function that in case of having isolated vertices to keep them, as is in the case of the edge #11 which has the vertex 202. Since we asked the function to create 5 copies of the adjacency matrix, we have a list of length 5 with adjacency matrices.  1 . . . . . .
# 103 . 1 .  1 .  1 . . 1
# 210 . . . . . 1 . 1 .
As you can see, the edgelist_to_adjmat
function kept the vertices labels and included them as dimnames in the matrix.2 Now that our adjacency matrix has the number of elements that we expected, which actually coincides with the number of rows in the fakesurvey
dataset, we can create a diffnet
object:
# Coercing the adjacency matrix and edgelist into a diffnet object
diffnet <- as_diffnet(
graph = adjmat, # Passing a dynamic graph
toa = fakesurvey$toa, # This is required
vertex.static.attrs = fakesurvey # Is is optional
)
# Taking a look at the diffnet object
diffnet
# Dynamic network of class -diffnet-
# Name : Diffusion Network
# Behavior : Unspecified
# # of nodes : 9 (101, 102, 103, 104, 105, 201, 202, 205, ...)
# # of time periods : 5 (1 - 5)
# Type : directed
# Final prevalence : 0.89
# Static attributes : id, toa, group, net1, net2, net3, age, gender, not... (9)
# Dynamic attributes : -
edgelist_to_diffnet
Following the previous example, instead of “manually” generating the adjacency matrix and calling the as_diffnet
function, we will use the edgelist_to_diffnet
function. The most important issue when calling this routine is to have matching ids between the edgelist and the attributes dataset. So before calling the edgelist_to_diffnet
function we need to fix the id
column in the fakesurvey
dataset:3
# [1] 1 2 3 4 5 1 2 5 10
# [1] 101 102 103 104 105 201 202 205 210
Now that it is fixed, we can call the edgelist_to_diffnet
function
diffnet2 <- edgelist_to_diffnet(
edgelist = fakeEdgelist[,1:2], # Passed to edgelist_to_adjmat
w = fakeEdgelist$value, # Passed to edgelist_to_adjmat
dat = fakesurvey, # Data frame with -idvar- and -toavar-
idvar = "id", # Name of the -idvar- in -dat-
toavar = "toa", # Name of the -toavar- in -dat-
keep.isolates = TRUE # Passed to edgelist_to_adjmat
)
# Warning in check_var_class_and_coerce(x, edgelist, c("factor", "integer", :
# Coercing -ego- into character.
# Warning in check_var_class_and_coerce(x, edgelist, c("factor", "integer", :
# Coercing -alter- into character.
# Dynamic network of class -diffnet-
# Name : Diffusion Network
# Behavior : Unspecified
# # of nodes : 9 (101, 102, 103, 104, 105, 201, 202, 205, ...)
# # of time periods : 5 (1 - 5)
# Type : directed
# Final prevalence : 0.89
# Static attributes : group, net1, net2, net3, age, gender, note (7)
# Dynamic attributes : -
As a difference with the previous example, here the algorithm makes sure that the ordering of the dataset and the vertices in the adjacency matrix coincide. The previous example did gave us a correctly sorted diffnet
object, but that may not always be the case. Nevertheless, the option id.and.per.vars
allows the user providing with the names of the variables in the vertex attribute datasets that hold the ids and time period ids of each observation, so that the function sorts the data before coercing it into diffnet objects. More on this in the following examples.
survey_to_diffnet
fakesurvey
, which holds cross section data, and fakesurveyDyn
, which holds longitudinal data.We start by taking a look at the data
# id toa group net1 net2 net3 age gender
# 1 1 1 1 NA NA NA 30 M
# 2 2 5 1 3 1 NA 35 F
# 3 3 5 1 NA 2 NA 31 F
# 4 4 3 1 6 5 NA 30 M
# 5 5 2 1 4 4 3 40 F
# 6 1 4 2 3 4 8 29 F
# 7 2 3 2 3 NA NA 35 M
# 8 5 3 2 10 1 NA 50 M
# 9 10 NA 2 5 1 NA 19 F
# note
# 1 No nominations
# 2 Nothing weird
# 3 Only nominates in net2
# 4 Nominates someone who wasn't interview
# 5 Nominates 4 two times
# 6 Only nominates outsiders
# 7 Isolated
# 8 Nothing weird
# 9 Non-adopter
A couple of important remarks for this dataset. First, each individual in this dataset belongs to a different group, while this is not always the case, survey_to_diffnet
allows accounting for this through the groupvar
argument. Also, besides of having an isolated vertex, two individuals in the survey nominate people that neither weren’t survey nor show in their groups:
# id toa group net1 net2 net3 age gender
# 4 4 3 1 6 5 NA 30 M
# 6 1 4 2 3 4 8 29 F
# note
# 4 Nominates someone who wasn't interview
# 6 Only nominates outsiders
So in group one 4 nominates id 6, who does not show in the data, and in group two 6 nominates 3, 4, and 8, also individuals who don’t show up in the survey.
While for some researchers nominations of unsurveyed individuals may not be of importance, for some others might be. For such cases, the function has the option of either keeping unsurveyed individuals (so you would get a bigger adjacency matrix), or ignore them and keep only those who were surveyed. For example, if we wanted to keep unsurveyed individuals in the network we would need to set no.unsurveyed = FALSE
:
# Coercing the survey data into a diffnet object
diffnet_w_unsurveyed <- survey_to_diffnet(
dat = fakesurvey, # The dataset
idvar = "id", # Name of the idvar (must be integer)
netvars = c("net1", "net2", "net3"), # Vector of names of nomination vars
toavar = "toa", # Name of the time of adoption var
groupvar = "group", # Name of the group var (OPTIONAL)
no.unsurveyed = FALSE # KEEP OR NOT UNSURVEYED
)
diffnet_w_unsurveyed
# Dynamic network of class -diffnet-
# Name : Diffusion Network
# Behavior : Unspecified
# # of nodes : 13 (101, 102, 103, 104, 105, 106, 201, 202, ...)
# # of time periods : 5 (1 - 5)
# Type : directed
# Final prevalence : 0.62
# Static attributes : group, net1, net2, net3, age, gender, note (7)
# Dynamic attributes : -
# [1] "101" "102" "103" "104" "105" "106" "201" "202" "203" "204" "205"
# [12] "208" "210"
A network spanning 5 time periods with 13 vertices (9 surveyed individuals + 4 unsurveyed individuals). This produces a different result when compared to the case in which me use the default behavior of the function, no.unsurveyed = TRUE
:
# Coercing the survey data into a diffnet object
diffnet_wo_unsurveyed <- survey_to_diffnet(
dat = fakesurvey, # The dataset
idvar = "id", # Name of the idvar (must be integer)
netvars = c("net1", "net2", "net3"), # Vector of names of nomination vars
toavar = "toa", # Name of the time of adoption var
groupvar = "group" # Name of the group var (OPTIONAL)
)
diffnet_wo_unsurveyed
# Dynamic network of class -diffnet-
# Name : Diffusion Network
# Behavior : Unspecified
# # of nodes : 9 (101, 102, 103, 104, 105, 201, 202, 205, ...)
# # of time periods : 5 (1 - 5)
# Type : directed
# Final prevalence : 0.89
# Static attributes : group, net1, net2, net3, age, gender, note (7)
# Dynamic attributes : -
# [1] "101" "102" "103" "104" "105" "201" "202" "205" "210"
Furthermore, we can compare the two diffusion networks by sustracting one from another:
# Dynamic network of class -diffnet-
# Name : Diffusion Network
# Behavior : Unspecified
# # of nodes : 4 (106, 203, 204, 208)
# # of time periods : 5 (1 - 5)
# Type : directed
# Final prevalence : 0.00
# Static attributes : group, net1, net2, net3, age, gender, note (7)
# Dynamic attributes : -
In this example we will use dynamic network data, this is, an edgelist with spells and dynamic attributes
# ego alter value time
# 1 102 101 1 1990
# 2 103 102 1 1990
# 3 102 103 1 1990
# 4 105 103 1 1990
# 5 105 104 2 1990
# 6 104 105 1 1990
# id toa group net1 net2 net3 age gender
# 1 1 1991 1 NA NA NA 30 M
# 2 2 1990 1 3 1 NA 35 F
# 3 3 1991 1 NA 2 NA 31 F
# 4 4 1990 1 6 5 NA 30 M
# 5 5 1991 1 4 4 3 40 F
# 6 1 1991 2 3 4 8 29 F
# note time
# 1 First wave: No nominations 1990
# 2 First wave: Nothing weird 1990
# 3 First wave: Only nominates in net2 1990
# 4 First wave: Nominates someone who wasn't interview 1990
# 5 First wave: Nominates 4 two times 1990
# 6 First wave: Only nominates outsiders 1990
Same as before, we have to make sure the ids are right
# Fixing ids
fakesurveyDyn$id <- with(fakesurveyDyn, group*100 + id)
# An individual who is alone
fakeDynEdgelist[11,"value"] <- 1
diffnet <- edgelist_to_diffnet(
edgelist = fakeDynEdgelist[,1:2], # As usual, a two column dataset
w = fakeDynEdgelist$value, # Here we are using weights
t0 = fakeDynEdgelist$time, # An integer vector with starting point of spell
t1 = fakeDynEdgelist$time, # An integer vector with the endpoint of spell
dat = fakesurveyDyn, # Attributes dataset
idvar = "id",
toavar = "toa",
timevar = "time",
keep.isolates = TRUE # Keeping isolates (if there's any)
)
# Warning in check_var_class_and_coerce(x, dat, c("numeric", "integer"),
# "integer", : Coercing -time- into integer.
# Warning in check_var_class_and_coerce(x, edgelist, c("factor", "integer", :
# Coercing -ego- into character.
# Warning in check_var_class_and_coerce(x, edgelist, c("factor", "integer", :
# Coercing -alter- into character.
# Dynamic network of class -diffnet-
# Name : Diffusion Network
# Behavior : Unspecified
# # of nodes : 9 (101, 102, 103, 104, 105, 201, 202, 205, ...)
# # of time periods : 2 (1990 - 1991)
# Type : directed
# Final prevalence : 1.00
# Static attributes : -
# Dynamic attributes : group, net1, net2, net3, age, gender, note (7)
While there are other candidates as the openxlsx
package, the readxl
package has the nice feature of correctly processing the encoding of the excel files. This is specially important if you are dealing with non ASCII or UTF-8 datasets.↩
Another thing to tell, the matrices stored in adjmat
are of class dgCMatrix
from the Matrix
package, these are Column Compressed Stored sparse matrices and allows saving memory in matrices with many zeros. netdiffuseR routines are based in this class of matrices. Furthermore, to have an idea of how much memory sparse matrices save, while a square matrix of size \(5e4\times 5e4\) would need close to 18GB of memory using a regular R matrix
, a dgCMatrix
of the same size takes around 6MB.↩
The with
function allows simplifying data management in R by allowing to reference columns in a data.frame without having to call the data.frame itself (see ?with
).↩


- sendLog(String) - Method in class io.branch.referral.BranchRemoteInterface
-
Log messages to the server's debug interface.
- ServerRequest - Class in io.branch.referral
-
Base class for all Branch API server requests.
- ServerRequest(String) - Constructor for class io.branch.referral.ServerRequest
-
Constructor class for instantiating a
ServerRequest
prior to adding JSON data.
- ServerRequest(String, JSONObject) - Constructor for class io.branch.referral.ServerRequest
-
Constructor class for instantiating a
ServerRequest
, with a
JSONObject
supplied containing post data in key-value pairs.
- ServerRequestQueue - Class in io.branch.referral
-
The Branch SDK can queue up requests whilst it is waiting for initialization of a session to
complete.
- ServerResponse - Class in io.branch.referral
-
Class providing the structure of a HTTP response as recieved from the Branch API.
- ServerResponse(String, int) - Constructor for class io.branch.referral.ServerResponse
-
Constructor method for the
ServerResponse
class that allows for the instantiation
of a server response object as a direct result of a server call.
- ServerResponse(String, int, BranchLinkData) - Constructor for class io.branch.referral.ServerResponse
-
Main constructor method for the
ServerResponse
class that allows for the instantiation
of a server response object as a direct result of a server call.
- setActionTotalCount(String, int) - Method in class io.branch.referral.PrefHelper
-
Sets the count of total number of times that the specified action has been carried out
during the current session, as defined in preferences.
- setActionUniqueCount(String, int) - Method in class io.branch.referral.PrefHelper
-
Sets the count of the unique number of times that the specified action has been carried
out during the current session, as defined in preferences.
- setAppKey(String) - Method in class io.branch.referral.PrefHelper
-
Deprecated.
- setAppVersion(String) - Method in class io.branch.referral.PrefHelper
-
- setBool(String, Boolean) - Method in class io.branch.referral.PrefHelper
-
Sets the value of the String
key value supplied in preferences.
- setCreditCount(int) - Method in class io.branch.referral.PrefHelper
-
Sets the credit count for the default bucket to the specified Integer
, in preferences.
- setCreditCount(String, int) - Method in class io.branch.referral.PrefHelper
-
Sets the credit count for the default bucket to the specified Integer
, in preferences.
- setDebug() - Method in class io.branch.referral.Branch
-
Sets the library to function in debug mode.
- setDebug() - Method in class io.branch.referral.PrefHelper
-
- setRandomizedDeviceToken(String) - Method in class io.branch.referral.PrefHelper
-
Sets the android.os.Build#RANDOMIZEDDEVICETOKEN
value of the current OS build, on the current device,
as a String
in preferences.
- setExternDebug() - Method in class io.branch.referral.PrefHelper
-
Switches external debugging on.
- setFloat(String, float) - Method in class io.branch.referral.PrefHelper
-
Sets the value of the String
key value supplied in preferences.
- setIdentity(String, Branch.BranchReferralInitListener) - Method in class io.branch.referral.Branch
-
Identifies the current user to the Branch API by supplying a unique identifier as a
String
value, with a callback specified to perform a defined action upon successful
response to request.
- setIdentity(String) - Method in class io.branch.referral.Branch
-
Identifies the current user to the Branch API by supplying a unique identifier as a
String
value.
- setIdentity(String) - Method in class io.branch.referral.PrefHelper
-
Sets the KEY_IDENTITY
String
value that has been set via the Branch API.
- setRandomizedBundleToken(String) - Method in class io.branch.referral.PrefHelper
-
Sets the KEY_RANDOMIZED_BUNDLE_TOKEN
String
value that has been set via the Branch API.
- setInstallParams(String) - Method in class io.branch.referral.PrefHelper
-
Sets the session parameters as originally set at time of app installation, in preferences.
- setInteger(String, int) - Method in class io.branch.referral.PrefHelper
-
Sets the value of the String
key value supplied in preferences.
- setIsReferrable() - Method in class io.branch.referral.PrefHelper
-
- setLinkClickID(String) - Method in class io.branch.referral.PrefHelper
-
- setLinkClickIdentifier(String) - Method in class io.branch.referral.PrefHelper
-
- setLong(String, long) - Method in class io.branch.referral.PrefHelper
-
Sets the value of the String
key value supplied in preferences.
- setNetworkCallbackListener(NetworkCallback) - Method in class io.branch.referral.BranchRemoteInterface
-
Sets a callback listener to handle network events received during this app session.
- setNetworkTimeout(int) - Method in class io.branch.referral.Branch
-
Sets the duration in milliseconds that the system should wait for a response before considering
any Branch API call to have timed out.
- setPost(Object) - Method in class io.branch.referral.ServerResponse
-
Sets the post data attached to the current server request, as a generic Object
instance.
- setRetryCount(int) - Method in class io.branch.referral.Branch
-
Sets the number of times to re-attempt a timed-out request to the Branch API, before
considering the request to have failed entirely.
- setRetryCount(int) - Method in class io.branch.referral.PrefHelper
-
Sets the value specifying the number of times that a Branch API call has been re-attempted.
- setRetryInterval(int) - Method in class io.branch.referral.Branch
-
Sets the amount of time in milliseconds to wait before re-attempting a timed-out request
to the Branch API.
- setRetryInterval(int) - Method in class io.branch.referral.PrefHelper
-
Sets the amount of time in milliseconds to wait before re-attempting a timed-out request
to the Branch API.
- setSessionID(String) - Method in class io.branch.referral.PrefHelper
-
Sets the ID of the KEY_SESSION_ID
String
value in preferences.
- setSessionParams(String) - Method in class io.branch.referral.PrefHelper
-
Sets the session parameters as currently set in preferences.
- setString(String, String) - Method in class io.branch.referral.PrefHelper
-
Sets the value of the String
key value supplied in preferences.
- setTimeout(int) - Method in class io.branch.referral.PrefHelper
-
Sets the duration in milliseconds to override the timeout value for calls to the Branch API.
- setUserURL(String) - Method in class io.branch.referral.PrefHelper
-
Sets the user URL from preferences.
- startTag - Static variable in class io.branch.referral.ApkParser
-
A static
Integer
that is used to identify the start of a XML tag
within a compressed XML document.
- SystemObserver - Class in io.branch.referral
-
Class that provides a series of methods providing access to commonly used, device-wide
attributes and parameters used by the Branch class, and made publically available for use by
other classes.
- SystemObserver(Context) - Constructor for class io.branch.referral.SystemObserver
-


This site is intended to host the My Information JSON file:
Soren Thrawl
[link]
Maddie Scherf
[link]
Starlord
[link]
Indiana Jones
[link]


Open, edit, save, and share Word, Excel, and PowerPoint files in the Microsoft 365 app. A subscription may be required for some features.


Across: 1. Left to gorge on food... (4 3 4) 9. ...Put out about f-food! (4) 10. This may breathe new life into green hill. About time! (11) 11. Fit hearts? (4) 14. Remains the prize for a series of tests (5) 17. Unknown person shows intelligence about head-gear (7) 19. Long-lasting real bud (7) 21. Mop four bends for scandalous minister (7) 22. Companion gets to twice go around the North (5) 23. Slim fired that man! (5) 24. Artist may be a help to old master (7) 27. The furthest point to meet Rex (7) 28. Stu tumbled and hit clump of grass! (7) 29. Jumble sale held right in Vincent's town (5) 31. Rule out bait (4) 34. Is Nell certain to make part of tube? (7,4) 35. Leon returns for Christmas (4) 36. Weeps about barren fruit (11) Down: 2. 5-in-1 cooker (4) 3. Need cultivated garden (4) 4. Band write song in garden, maybe... (4 3) 5. ...Grind for one of The Hollies? (5) 6. Work on big problem for animal (7) 7. Trick old city club to back leader from built-up area (11) 8. Sweaters for crusty swimmers? (11) 12. Wizard sent about £ to country (11) 13. Toured successfully in Shakespeare's monster play! (11) 15. Not cold prospect for Cork (7) 16. Teach eggheads about old coin (7) 18. Prose written about seed? (5) 20. Car turns over! (5) 25. A scholar and true gentleman! (7) 26. Susan returns to Turks Head with one man (7) 30. Pounds may be universal weight in this place (5) 32. Smear best lard under ribs, initially (4) 33. Ken danced with energy in joint (4)


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Returns a new type that is the result of adding the rules of one type to another.
Sets a default value if the original value is undefined.
the value. value supports references. value may also be a function which returns the default value. If value is specified as a function that accepts a single parameter, that parameter will be a context object that can be used to derive the resulting value. This clones the object however, which incurs some overhead so if you don't need access to the context define your method so that it does not accept any parameters. Without any value, default has no effect, except for object that will then create nested defaults (applying inner defaults of that object).
Note that if value is an object, any changes to the object after default() is called will change the reference and any future assignment.
Additionally, when specifying a method you must either have a description property on your method or the second parameter is required.
Returns a plain object representing the schema's rules and properties
Annotates the key
Considers anything that matches the schema to be empty (undefined).
any object or joi schema to match. An undefined schema unsets that rule.
Overrides the default joi error with a custom error if the rule fails where:
can be:
an instance of Error
- the override error.
a function(errors)
, taking an array of errors as argument, where it must either:
return a string
- substitutes the error message with this text
return a single object
or an Array
of it, where:
type
- optional parameter providing the type of the error (eg. number.min
).
message
- optional parameter if template
is provided, containing the text of the error.
template
- optional parameter if message
is provided, containing a template string, using the same format as usual joi language errors.
context
- optional parameter, to provide context to your error if you are using the template
.
return an Error
- same as when you directly provide an Error
, but you can customize the error message based on the errors.
Note that if you provide an Error
, it will be returned as-is, unmodified and undecorated with any of the
normal joi error properties. If validation fails and another error is found before the error
override, that error will be returned and the override will be ignored (unless the abortEarly
option has been set to false
).
Annotates the key with an example value, must be valid.
Marks a key as forbidden which will not allow any value except undefined. Used to explicitly forbid keys.
Blacklists a value
Overrides the key name in error messages.
Attaches metadata to the key.
Annotates the key
Marks a key as optional which will allow undefined as values. Used to annotate the schema for readability as all keys are optional by default.
Overrides the global validate() options for the current key and any sub-key.
Outputs the original untouched value instead of the casted value.
Marks a key as required which will not allow undefined as value. All keys are optional by default.
Sets the options.convert options to false which prevent type casting for the current key and any child keys.
Marks a key to be removed from a resulting object or array after validation. Used to sanitize output.
Annotates the key
Annotates the key with an unit name.
Adds the provided values into the allowed whitelist and marks them as the only valid values allowed.
Validates a value using the schema and options.
Converts the type into an alternatives type where the conditions are merged into the type definition where:
Generated using TypeDoc
Whitelists a value


--- layout: post title: Cyborg Life ~ How Scientists Gave Robotic Arms The Ability to Feel thumbnail: /img/robotic-arm.jpg ---
For the first time, paralyzed patients can "feel" things they touch with their brain-controlled robotic arm. Scientists at The University of Chicago have developed a brain implant that turns sensor data from robotic arms into "feeling" in the brain, by using electricity to stimulate the somatosensory cortex of the brain.
After years of research and testing, a month ago a tetraplegic patient received the first of these touch-restoring implants.
Touch is not the first sense to be restored by the ever-growing field of "neuro-prosthetics", electronic devices that not only mimic human functions, but interface with the body:
Cochlear implants, which work by recording sound, converting it into electric signals, and passing it on to the auditory nerve, have restored hearing to 300,000 patients since their introduction. Visual neuro-prosthetics in trial stages are starting to restore functional sight to blind patients.
But this discovery is so much bigger...
Right now, anyone who is tetraplegic requires constant care. They cannot walk, or grab things, or dress themselves. This places a huge burden of cost and time on their families, and places a huge psychological burden, guilt, on those who suffer from this.
Neuro-prosthetic arms allow these patients to do some things for themselves, and may eventually allow them to live independently.
Why is the sensation touch so important? Without the sensation of touch , artificial limbs have been very difficult to control. Studies show that even basic tasks like lighting a match can be very difficult, if the thumb and forefinger are numbed.
That's why this advancement is so huge. With the sensation of touch, patients may one day be able to complete difficult, pressure-sensitive tasks such as painting, or gripping an egg.
It took a lot of time and effort, even after finding the technology to be feasible, to get to this point. Scientists had to find the electrical signals that mimicked the sensation of touch. They had to find a way to map pressure sensors to electrical signals, and convert any level of pressure into a feeling, and they needed to make sure it was safe.
The question is no longer whether it's feasible. The question is how far this technology can go. Dr. Sliman Bensmaia, one of the lead researchers on this project for The University of Chicago, believes that one day robotic arms could surpass the human arm in terms of strength, responsiveness, and speed.
But that's down the road. For now, we'll just have to settle for restoring autonomy to the lives of a few paraplegic folks. That's pretty good.
For more in-depth information: Read the study behind this breakthrough.


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SECTION: 200-General TITLE: apache QUESTION: Can Jetty be run with Apache? Firstly remember that Jetty is a HTTP/1.1 server and there is no need to use another webserver just to serve static content. However, if existing applications or special features require Apache to be used, it is possible to use Jetty behind Apache.
The setup uses the Apache transparent proxy feature. If you have an Apache server with DSO support or mod_proxy is already configured in, you may not have to rebuild Apache. You may also wish to use Apache's mod_rewrite. Some Apache installations in RedHat RPM format have everything you will need, right out of the box. These instructions presume that you have an Apache with the appropriate modules compiled in or available as DSO's for dynamic loading.
Configuring Jetty
Jetty's configuration need not change for this setup. Just use a different port than Apache.
Enabling Apache Dynamic Modules
If the modules you need are compiled in, omit this step. To enable the DSO versions of the modules, you will need to enable them with lines in your configuration file (httpd.conf) such as:
AddModule mod_rewrite.c # Optional
AddModule mod_proxy.c
Using Apache's Transparent Proxying
LoadModule rewrite_module modules/mod_rewrite.so # Optional
LoadModule proxy_module modules/libproxy.so
Apache's transparent proxy facility enables it to delegate HTTP requests to another web server such as Jetty. The simplest configuration gives Jetty all requests for certain parts of your site, for example all URLs starting with /foo/ or /bar/. If these parts of your site do not contain a lot of heavily-accessed static content such as images and other files, this is fine. You will not require mod_rewrite for this configuration. Just put one or more lines in your Apache config file such as:
In this example, Apache will delegate responsibility for all pages in the
/foo/ area of your site to Jetty on port 8080. In case some of the pages served by Jetty do redirects, it is probably wise to add an additional line:
ProxyPass /foo/ http://localhost:8080/foo/
Jetty could be running on a different machine, in which case substitute the name of that machine for "localhost" in both the lines above. You can use as many ProxyPass directives as needed. The one ProxyPassReverse line will fix up all redirect requests from Jetty to point to Apache, so your Jetty server won't become visible to visitors to your site.
ProxyPassReverse / http://localhost:8080/
You will not need mod_rewrite for this type of setup.
Transparent Proxying with mod_rewrite
If pages to be served by Jetty and by Apache are heavily intermixed, you can use mod_rewrite for finer control over Apache's decision whether to delegate the request to Jetty.
[[To be written.]]
Jetty with Apache with SSL
If your Apache server supports SSL, the transparent proxy feature will automatically handle SSL. It converts SSL requests to ordinary HTTP requests and encrypts the responses for you.
Performance Pros and Cons
If you prefer to use Apache to serve some of your content, or if you are constrained for any reason to using Apache, you can run Jetty just for the content you prefer to serve with Jetty. If you simply wish to optimize performance, you may need to experiment to find the best approach. Apache is probably faster than Jetty for static content, but routing requests through Apache slows them down somewhat also. Apache's transparent proxy feature claims only HTTP 1.0 compliance, so serving content directly with Jetty may reduce the number of TCP connections that must be established with the web browser.
We would love to learn of your results with performance testing.
Limitations
The Servlet API calls ServletRequest.getRemoteHost() and ServletRequest.getRemoteAddr() will refer to the host running Apache instead of the IP address of the remote user (or their proxy server). This limitation could be worked around in the future.
You may also wish to turn off Jetty's HTTP request logging. The limitation on access to remote addresses applies to Jetty logging also.


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This elements uses a 2D array to build an HTML table. The table has many optional features including searching, sorting, CSV/excel/PDF downloading, paging, complex data, buttons etc.
A simple button with some built in colors and sizes.
A nicer looking check box.
A nicer looking text box with built in regex based validation
A nicer drop down menu with built in regex based validation
A nicer looking alert box, with a built in timer.
A group of button that work with tables and 2D arrays to build csv/excel/pdf files, print the information and copy information to the clipboard.
A set of global static objects designed to help object and elements communicate universally.


Deep Learning for Named Entity Recognition: A Comprehensive Survey
A survey on deep learning in named entity recognition.
Named entity recognition (NER) is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories (e.g., organization, location and person). Recently, deep learning has been broadly applied to solve NER problem with great success. Thus, I write up this survey to summarize recent work.
- Word-level CNN Encoder, CRF Tag Decoder
- Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, Pavel P. Kuksa. Natural Language Processing (Almost) from Scratch. JMLR 2011. PDF
- Jingwei Zhuo, Yong Cao, Jun Zhu, Bo Zhang, Zaiqing Nie. Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields. ACL 2016. PDF
- Emma Strubell, Patrick Verga, David Belanger, Andrew McCallum. Fast and Accurate Entity Recognition with Iterated Dilated Convolutions. EMNLP 2017. PDF
- Word-level RNN Encoder, CRF/MEM Tag Decoder
- Zhiheng Huang, Wei Xu, Kai Yu. Bidirectional LSTM-CRF Models for Sequence Tagging. ICLR 2018. PDF
- Xuezhe Ma, Eduard H. Hovy. End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF. ACL 2018. PDF
- Marek Rei, Gamal K. O. Crichton, Sampo Pyysalo. Attending to Characters in Neural Sequence Labeling Models. Coling 2016. PDF
- Fabrice Dugas, Eric Nichols. DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets. NUT@Coling 2016. PDF
- Jason P. C. Chiu, Eric Nichols. Named Entity Recognition with Bidirectional LSTM-CNNs. TACL 2016. PDF
- Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer. Neural Architectures for Named Entity Recognition. HLT-NAACL 2016. PDF
- Thien Huu Nguyen, Avirup Sil, Georgiana Dinu, Radu Florian. Toward Mention Detection Robustness with Recurrent Neural Networks. https://arxiv.org/pdf/1602.07749.pdf. 2016. PDF
- Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, Russell Power. Semi-supervised Sequence Tagging with Bidirectional Language Models. ACL 2017. PDF
- Quan Tran, Andrew MacKinlay, Antonio Jimeno-Yepes. Named Entity Recognition with Stack Residual LSTM and Trainable Bias Decoding. IJCNLP 2017. PDF
- Bill Y. Lin, Frank F. Xu, Zhiyi Luo, Kenny Q. Zhu. Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media. NUT@EMNLP 2017. PDF
- Nils Reimers, Iryna Gurevych. Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging. EMNLP 2017. PDF
- Liyuan Liu, Jingbo Shang, Xiang Ren, Frank Fangzheng Xu, Huan Gui, Jian Peng, Jiawei Han. Empower Sequence Labeling with Task-Aware Neural Language Model. AAAI 2018. PDF
- Abbas Ghaddar and Philippe Langlais. Robust Lexical Features for Improved Neural Network Named-Entity Recognition. arXiv 2018. PDF
- Word-level RNN Encoder, RNN Tag Decoder
- Suncong Zheng, Feng Wang, Hongyun Bao, Yuexing Hao, Peng Zhou, Bo Xu. Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme. ACL 2017. PDF
- Suncong Zheng, Yuexing Hao, Dongyuan Lu, Hongyun Bao, Jiaming Xu, Hongwei Hao, Bo Xu. Joint Entity and Relation Extraction Based on A Hybrid Neural Network. Neurocomputing 2017. PDF
- Peng Zhou, Suncong Zheng, Jiaming Xu, Zhenyu Qi, Hongyun Bao, Bo Xu. Joint Extraction of Multiple Relations and Entities by Using a Hybrid Neural Network. CCL 2017. PDF
- Zhi-Xiu Ye, Zhen-Hua Ling. Hybrid semi-Markov CRF for Neural Sequence Labeling. ACL 2018. PDF
- Meizhi Ju, Makoto Miwa, Sophia Ananiadou. A Neural Layered Model for Nested Named Entity Recognition. NAACL-HLT 2018. PDF
- Word-level RNN Encoder, Pointer Tag Decoder
- Feifei Zhai, Saloni Potdar, Bing Xiang, Bowen Zhou. Neural Models for Sequence Chunking. AAAI 2017. PDF
- Fixed-size Ordinally Forgetting Encoding, Feedforward Neural Network
- Mingbin Xu, Hui Jiang, Sedtawut Watcharawittayakul. A Local Detection Approach for Named Entity Recognition and Mention Detection. ACL 2017. PDF
- Multi-task Learning
- Zhilin Yang, Ruslan Salakhutdinov, William W. Cohen. Multi-Task Cross-Lingual Sequence Tagging from Scratch. https://arxiv.org/pdf/1603.06270.pdf, 2016. PDF
- Marek Rei. Semi-supervised Multitask Learning for Sequence Labeling. ACL 2017. PDF
- Active Learning
- Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar. Deep Active Learning for Named Entity Recognition. ICLR 2018. PDF
- Transfer Learning
- Zhilin Yang, Ruslan Salakhutdinov, William W. Cohen. Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks. ICLR 2017. PDF
- Reinforcement Learning
- Karthik Narasimhan, Adam Yala, Regina Barzilay. Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning. EMNLP 2016. PDF
- Deep Learning based NER in Medical Text
- Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Elke A. Rundensteiner, Xiangnan Kong. Bidirectional LSTM-CRF for Adverse Drug Event Tagging in Electronic Health Records. PMLR 2016. PDF
- Ken Yano. Neural Disease Named Entity Extraction with Character-based BiLSTM+ CRF in Japanese Medical Text. arxiv 2018. PDF
- Tools
- Franck Dernoncourt, Ji Young Lee, Peter Szolovits. NeuroNER: An Easy-to-Use Program for Named-entity Recognition Based on Neural Networks. EMNLP 2017. Website


Brutal axe attack in donkey row
A firewood vendor from Mkoba 4 suburb in Gweru is battling for life at Gweru Provincial Hospital after he was allegedly attacked with an axe all over his body by a colleague following a misunderstanding over a donkey.
by Mejury MagayaActing Midlands provincial police spokesperson, Assistant Inspector Ethel Mukwende, said police in Gweru have launched a manhunt for Munyaradzi Chekecha over the attack on Kelvin Makuswane over a borrowed donkey.
She said sometime this month, Chekecha allegedly assaulted Makuswane (33) of Mkoba 4 Gweru following a misunderstanding over a donkey which the latter had borrowed and didn’t return.
“However, Makuswane did not report the matter to the police. They were both firewood vendors at Mkoba 6 Business Centre, Gweru,” she said.
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On November 17 at around 8PM, Asst Insp Mukwende said, Makuswane was allegedly walking from Mkoba 6 Business Centre going home and met Chekecha where they sell firewood.
She said Chekecha allegedly followed him while armed with an axe.
Asst Insp Mukwende said Makuswane ran away but Chekecha caught up with him and allegedly struck him with the axe several times.
After committing the offence he ran away.
She said Makuswane was taken to ZRP Mkoba 1 by a Good Samaritan.
“Makuswane sustained serious injuries on his body and was referred to Gweru Provincial Hospital where he is battling for his life,” said Asst Insp Mukwende.
She said police are appealing to members of the public who might have information that may lead to the arrest of Chekecha to report at any police station or make use of police suggestion boxes near them.
“Once again the police are appealing to members of the public to solve their disputes or differences amicably by engaging a third party so as to be able to manage their issues. Use of violence constitutes an offence and leads to unnecessary loss of live,” she said. The Chronicle


|
API JSON Reference
4.1
|
This doxygen describes the JSON schema used in the Nymi API (NAPI). The JSON protocol is asynchronous.
XXXX: napi::RequestEnvelope
JSON keys are strings with the names of the request and response attributes.
The types of the attributes are the types expected in JSON.
In the case of attributes with enum types, the JSON value should be a string containing the name of the enum.
All attributes in a request are required unless a default value is specified, in which case that value is used if the attribute is not in the JSON request.
If an attribute in a response has a default, it will always have that value in the JSON response.
These terms are used frequently in the code. Instead of defining them each time they occur, we have provided them in the list below. You should quickly become familiar with these terms.
| General | |
|---|---|
| napi::InfoGet | Request information describing the current state of NAPI, provisions, and the currently visible Nymi Bands. |
| napi::InitGet | Request information regarding the configuation state of the NAPI system. |
| napi::NotificationGet | Request the current state of notification subscriptions. |
| napi::NotificationSet | Request subscriptions to the specified notifications. |
Provisioning | |
| napi::ProvisionRunStart | Start provisioning of Nymi Bands (when the Nymi Band goes into provisioning mode). |
| napi::ProvisionRunStop | Stop provisioning of Nymi Bands. |
| napi::ProvisionPattern | Inform the provisioning process of the LED pattern selected by the user. |
| napi::EventOnLEDPatternChangeData | Notify the NEA of the currently known provisioning LED patterns. |
| napi::EventOnProvisionedData | Notification of a successful provisioning. |
Any Nymi Band | |
| napi::RoamingAuthRun | Initiate the Roaming Authentication process. |
| napi::RoamingAuthSig | Inform Nymi Band of the signature of the nonce, a fresh server nonce, and the partner public key. |
Provisioned Nymi Bands | |
| napi::Buzz | Buzz (notify) the Nymi Band to indicate to the user an outcome of an NEA-specific operation. |
| napi::CDFGet | Request the value of the CDF key. If you require this function, please contact Nymi for details. |
| napi::CDFRun | Request to create a CDF key. |
| napi::EventRANonceData | Notification of a fresh nonce to be signed by the Roaming Authenticator service. |
| napi::KeyDelete | Request certain keys to be deleted from the provision. |
| napi::RandomRun | Request a pseudo-random number. |
| napi::RevokeRun | Request that a provision be revoked (from a Nymi Band). |
| napi::RoamingAuthSetup | Request to Setup Roaming Authentication. |
| napi::SignSetup | Request to setup the local signing capability. |
| napi::SignRun | Request to sign a SHA256 hash. |
| napi::SymmetricKeyGet | Request the value of the symmetric key. |
| napi::SymmetricKeyRun | Request that a symmetric key be generated. |
| napi::TOTPGet | Request the value of the TOTP. |
| napi::TOTPRun | Request to create a TOTP. |
Notifications | |
| napi::EventOnFoundChangeData | onFoundChange notification |
| napi::EventOnGeneralErrorData | onGeneralError notification |
| napi::EventOnPresenceChangeData | onPresenceChange notification |
| napi::EventOnProvisionsChangedData | onProvisionsChanged notification |
Note: This document may receive additional updates.


This demonstrates the use of an alternate colormap to see more detailed pattern gradients within data.


礦ision Build Log
Tool Versions:
IDE-Version: μVision V<IP_ADDRESS> Copyright (C) 2016 ARM Ltd and ARM Germany GmbH. All rights reserved. License Information: 789 Dreamer, 852, LIC=AT28T-R7C93-USPYZ-JILZU-JUCX3-DW4MP Tool Versions: Toolchain: PK51 Prof. Developers Kit Version: <IP_ADDRESS> Toolchain Path: D:\Program Files (x86)\Keil\C51\BIN C Compiler: C51.exe V<IP_ADDRESS> Assembler: A51.exe V<IP_ADDRESS> Linker/Locator: BL51.exe V6.22 Library Manager: LIB51.exe V<IP_ADDRESS> Hex Converter: OH51.exe V<IP_ADDRESS> CPU DLL: S8051.DLL V<IP_ADDRESS> Dialog DLL: DP51.DLL V<IP_ADDRESS> Target DLL: STCMON51.DLL V1, 0, 2, 0 Dialog DLL: TP51.DLL V<IP_ADDRESS>Project:
D:\微机原理\微机原理实验\键盘与LED\key&LED.uvproj Project File Date: 12/14/2017Output:
Build target 'Target 1' compiling usart.c... linking...  ".\Objects\keyLED" - 0 Error(s), 28 Warning(s). Build Time Elapsed: 00:00:01


A collection of tools used to generate various types of random portfolios. The weights of these portfolios are random variables derived from truncated continuous random variables.
| Version: | 1.0-1 |
| Depends: | R (≥ 2.0.1), truncdist |
| Published: | 2016-08-19 |
| Author: | Frederick Novomestky |
| Maintainer: | Frederick Novomestky <fn334 at nyu.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | rportfolios results |
| Reference manual: | rportfolios.pdf |
| Package source: | rportfolios_1.0-1.tar.gz |
| Windows binaries: | r-devel: rportfolios_1.0-1.zip, r-release: rportfolios_1.0-1.zip, r-oldrel: rportfolios_1.0-1.zip |
| OS X binaries: | r-release: rportfolios_1.0-1.tgz, r-oldrel: rportfolios_1.0-1.tgz |
| Old sources: | rportfolios archive |
Please use the canonical form https://CRAN.R-project.org/package=rportfolios to link to this page.


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Code by Randall Baxley offered as Public Domain


Updates to my street sweeping app 2008/09/21
It's been a long time coming but I've finally updated Where's my car? to take recent updates over at SF GIS into account. They've augmented the street sweeping schedule in most places to include something to the effect of "Effective 8/25/2008, 1st & 3rd Tuesday only" which will reduce the "false positive" problem with the site. Now the site will be very explicit about the date and time you should move your car instead of relying on "Mon 9AM" to get the point across.
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(Follow the links to learn more about each backend.)
| Oracle | PostgreSQL | MySQL | SQLite3 | Firebird | ODBC | DB2 | |
|---|---|---|---|---|---|---|---|
| Binding by Name | YES | YES (>=8.0) | YES | YES | YES | YES | YES |
| Dynamic Binding | YES | YES | YES | YES | YES | YES | |
| Bulk Operations | YES | YES | YES | YES | YES | YES | YES |
| Transactions | YES | YES | YES (with servers that support them, usually >= 4.0) | YES | YES | YES | YES |
| BLOB Data Type | YES | YES | MySQL's BLOB type is mapped to std::string |
YES | YES | NO | NO |
| RowID Data Type | YES | YES | NO | NO | NO | NO | NO |
| Nested Statements | YES | NO | NO | NO | NO | NO | YES |
| Stored Procedures | YES | YES | NO (but stored functions, YES) | NO | YES | NO | YES |
| Previous (Rationale FAQ) |
Copyright © 2013 Mateusz Loskot
Copyright © 2004-2008 Maciej Sobczak, Stephen Hutton


Journal of the Mathematical Society of Japan, vol. 57 (2005)
- AUDOUBERT Benoît, NGUYEN Tu Chanh, OKA Mutsuo,
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An analogue of Connes-Haagerup approach for classification of subfactors of type III$_{\bf 1}$
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- FUKUMA Yoshiaki,
A generalization of the $\Delta$-genus of quasi-polarized varieties
- Journal of the Mathematical Society of Japan, vol. 57 pp. 1003-1044 (2005);
- TAHARA Hidetoshi,
Uniqueness of the solution of nonlinear totally characteristic partial differential equations
- Journal of the Mathematical Society of Japan, vol. 57 pp. 1045-1065 (2005);
- OKABE Yasunori, MATSUURA Masaya,
On non-linear filtering problems for discrete time stochastic processes
- Journal of the Mathematical Society of Japan, vol. 57 pp. 1067-1076 (2005);
- DOWBOR Piotr,
A construction of non-regularly orbicular modules for Galois coverings
- Journal of the Mathematical Society of Japan, vol. 57 pp. 1077-1127 (2005);
- DUONG Xuan Thinh, YAN Lixin,
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 1129-1152 (2005);
- KAJI Shizuo,
On the nilpotency of rational $\bm{H}$-spaces
- Journal of the Mathematical Society of Japan, vol. 57 pp. 1153-1165 (2005);
- KANEMITSU Shigeru, TANIGAWA Yoshio, YOSHIMOTO Masami,
Convolution of Riemann zeta-values
- Journal of the Mathematical Society of Japan, vol. 57 pp. 1167-1177 (2005);
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 619-637 (2005);
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The strange aspect of most compacta
- Journal of the Mathematical Society of Japan, vol. 57 pp. 701-708 (2005);
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 753-779 (2005);
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On harmonic function spaces
- Journal of the Mathematical Society of Japan, vol. 57 pp. 781-802 (2005);
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Lagrangian calculus on Dirac manifolds
- Journal of the Mathematical Society of Japan, vol. 57 pp. 803-825 (2005);
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 309-332 (2005);
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 357-385 (2005);
- MURATA Minoru,
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 387-413 (2005);
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 415-460 (2005);
- MUKAI Juno, YAMAGUCHI Kohhei,
Homotopy classification of twisted complex projective spaces of dimension 4
- Journal of the Mathematical Society of Japan, vol. 57 pp. 461-489 (2005);
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 491-512 (2005);
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 513-521 (2005);
- KURIHARA Masayuki, SAKAI Katsuro, YAGUCHI Masato,
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 523-535 (2005);
- KAMADA Seiichi, MATSUMOTO Yukio, MATUMOTO Takao, WAKI Keita,
Chart description and a new proof of the classification theorem of genus one Lefschetz fibrations
- Journal of the Mathematical Society of Japan, vol. 57 pp. 537-555 (2005);
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Laplace approximations for large deviations of diffusion processes on Euclidean spaces
- Journal of the Mathematical Society of Japan, vol. 57 pp. 557-592 (2005);
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Homogenization for stochastic partial differential equations derived from nonlinear filterings with feedback
- Journal of the Mathematical Society of Japan, vol. 57 pp. 593-603 (2005);
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 21-36 (2005);
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- Journal of the Mathematical Society of Japan, vol. 57 pp. 37-54 (2005);
- MASHIKO Yukihiro, NAGANO Koichi, OTSUKA Kazuo,
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Weierstrass-type representation for harmonic maps into general symmetric spaces via loop groups
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The finite group action and the equivariant determinant of elliptic operators
- Journal of the Mathematical Society of Japan, vol. 57 pp. 95-113 (2005);
- WAKATSUKI Satoshi,
The Igusa local zeta function of the simple prehomogeneous vector space ${(GL(1)^4\times SL(2n+1),\Lambda_2\oplus\Lambda_1\oplus\Lambda_1\oplus\Lambda_1)}$
- Journal of the Mathematical Society of Japan, vol. 57 pp. 115-126 (2005);
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Zero sets of solutions of second order parabolic equations with two spatial variables
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On Mordell-Weil lattices of bielliptic fibrations on rational surfaces
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A remark on Schubert cells and the duality of orbits on flag manifolds
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Continuous dependence on a parameter of exponential attractors for chemotaxis-growth system
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Weak type (1, 1) bounds for a class of the Littlewood-Paley operators
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A characterization of symmetric cones through pseudoinverse maps
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- HASHIMOTO Yasufumi,
Special values of the spectral zeta functions for locally symmetric Riemannian manifolds
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Deficiencies of meromorphic mappings for hypersurfaces
- Journal of the Mathematical Society of Japan, vol. 57 pp. 233-258 (2005);
- SCHMIDT Ralf,
Iwahori-spherical representations of GSp(4) and Siegel modular forms of degree 2 with square-free level
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SUBMARINE X-1 (director: William Graham; screenwriter: story by John C. Champion and Edmund North/Donald S. Sanford/Guy Elmes; cinematographer: Paul Beeson; editor: John S. Smith; cast: James Caan (Commander Bolton), Rupert Davies (Vice-Admiral Redmayne), David Sumner (Lt. Davies, RNVR), William Dysart (Lt. Gogan, RNR), Paul Young (Leading Seaman Quentin), Norman Bowler (Sub. Lt. Pennington); Runtime: 90; United Artsists; 1968-UK)
Reviewed by Dennis Schwartz
A routine WW11 submarine action picture, featuring an unknown cast, though in a few years the film's star, James Caan, was to get his breakthrough role in "The Godfather." The film was shot on location in the north of Scotland, which substituted for the fjord in Norway, where this actual story took place.
James Caan plays Lt. Commander Bolton, a stern, no-nonsense, gung-ho sailor. He is just coming out of a naval inquiry as to his decision to attack the heavily fortified German battleship 'Lidendorf' in a Norwegian fjord, whereby his submarine was severely damaged and 50 of his men lost their lives. He is cleared of reckless error in judgment charges and is called into the office of Vice-Admiral Redmayne (Rupert Davies) to find out his next assignment. He is surprisedly given a promotion and told that his next assignment is a top-secret mission, where he is in charge of a training program in Scotland. He will train three volunteer specially picked crews for a commando mission to go through the nets and attack the German battleship again, but this time with three experimental small submarines given the name X -- whose letter stands for experiment. The subs are 51 feet long, weigh 35 tons, and have a depth of 300 feet, and will take a four-men crew. On each side of the boat will be a deadly explosive amatol device.
Half of the film concerns the serious training the men undergo under the watchful eye of the stern Bolton. The usual war film clichés are present in large doses.
At the half-way mark, the first action occurs, as the German paratroopers attack the secret training spot. The high command realizes after the attack that their position has been compromised and they rush the training through, as they want to attack the feared battleship while there is still an element of surprise about their secret subs. They are also afraid that if they wait any longer, the Germans will lauch a full-scale attack on the site and destroy their subs.
As the men cut through the nets in the fjord, they are intercepted by German frogmen, who capture three of them. It then becomes Bolton's decision of if and when to attack the 'Lidendorf.'
A watchable but forgettable film, with nothing in it that is distinguishable. It's a well crafted actioner with a terse and fast-paced story, and should be acceptable for those who don't mind that the movie is a cliché.
REVIEWED ON 11/13/2000 GRADE: C
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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Northeast State Community College
Northeast State Community College is a comprehensive two-year community college under the governance of the Tennessee Board of Regents of the State University and Community College System of Tennessee. As a comprehensive community college, Northeast State provides university parallel programs designed for students desiring to transfer to another college or university, career programs for students planning to enter the workforce immediately upon graduation, and continuing education and community service programs for professional growth and personal enrichment to the citizens of Carter, Johnson, Sullivan, Unicoi and Washington counties.
Accreditation
Northeast State is accredited by the Southern Association of Colleges and Schools Commission on Colleges to award the associate degree. Contact the Commission on Colleges at 1866 Southern Lane, Decatur, GA 30033-4097 or call<PHONE_NUMBER> for questions about the accreditation of Northeast State.


Fast routine for box, max and min filtering. Same effect as calling 'C=convn( I, ones(dims), shape)', except more efficient. Computes local sums by using running sums. To get sum in non-overlapping windows, use shape='block'. Equivalent to doing localSum, and then subsampling (except more efficient). If operation op is set to 'max' or 'min', computes local maxes or mins instead of sums. Note, that when applicable convBox and convMax are significantly faster. USAGE I = localSum( I, dims, [shape], [op] ) INPUTS I - matrix to compute sum over dims - size of volume to compute sum over, can be scalar shape - ['full'] 'valid', 'full', 'same', or 'block' op - ['sum'] 'max', or 'min' OUTPUTS C - matrix of sums EXAMPLE - 1 A=rand(500,500,1); dim=25; f=ones(dim,1); shape='same'; r=20; tic, for i=1:r, B = localSum(A,dim,shape); end; toc tic, for i=1:r, C = conv2(conv2(A,f,shape),f',shape); end; toc diff=B-C; im(diff), sum(abs(diff(:))) EXAMPLE - 2 load trees; I=ind2gray(X,map); figure(1); im(I); I1=localSum(I,3,'block','sum'); figure(2); im(I1); title('sum') I2=localSum(I,3,'block','max'); figure(3); im(I2); title('max') I3=localSum(I,3,'block','min'); figure(4); im(I3); title('min') See also convBox, convMax, imShrink Piotr's Computer Vision Matlab Toolbox Version 3.22 Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] Licensed under the Simplified BSD License [see external/bsd.txt]


The panther is a certain animal that discharges an odor so that savage animals, like the lion, the wolf and the leopard, who are unwilling to harm the panther, upon sensing this odor, immediately become gentle and no longer attack it.
Moral. There are some who are so kind in words and deeds so that even their enemies, on hearing them, soothed by their sweet speech, sweep away their rage and hatred and esteem them. From which Solomon in an allegory: "A soft answer turneth away wrath; a harsh word rouses anger"; and again: "A good word is better than a gift."